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Fast-Folding Experiments and the Topography of Protein Folding Energy Landscapes PG Wolynes’, Z Luthey-Schultenl and JN Onuchic*

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Fast-Folding Experiments and the Topography of Protein Folding Energy Landscapes PG Wolynes’, Z Luthey-Schultenl and JN Onuchic* Review 425 Fast-folding experiments and the topography of protein folding energy landscapes PG Wolynes’, Z Luthey-Schultenl and JN Onuchic* The rapid folding of certain proteins can be described Introduction theoretically using an energy landscape in the shape of a For those who appreciate the beauty of the architecture of folding funnel. New techniques have allowed the examination folded proteins it is amazing to find that such complex of fast-folding events that occur in microseconds or less, and structures can be formed spontaneously in biologically have tested the predictions of the theoretical models. short times, although doing so is clearly an evolutionary necessity. In human affairs, complex organized structures Addresses: ‘School of Chemical Sciences, University of Illinois, have always been made by a series of individually simple Urbana, IL 61801, USA and 2Department of Physics, University of California, San Diego, La Jolla, CA 92093, USA. steps. A similar picture of the folding process seemed to emerge from the classical kinetic studies of folding on time Chemistry & Biology June 1996,3:425-432 scales greater than a few milliseconds [l]. But it was real- ized that, particularly for smaller proteins, folding could 0 Current Biology Ltd ISSN 1074-5521 occur far more rapidly and without a requirement for this discrete stepwise assembly. This led to a view of protein folding based on a statistical mechanical characterization of the energy landscape of a folding protein ([Z] and refer- ences therein), and this viewpoint has received consider- able support from computational studies of model proteins [3-81. With this new perspective it becomes clear that we will only be able to directly unravel the basic mechanisms of biomolecular self-organization and measure the topogra- phy of the folding-energy landscape with a new generation of experiments using relatively newer techniques that are capable of monitoring protein folding on shorter time scales (lasers [9-131, NMR [14-191, ultrafast mixing [ZO] and protein engineering [21,22]). This review highlights a remarkable fast folding experiment described in this issue by Mines et al! [23], who show experimentally a regularity in the folding of two proteins that is expected from a statis- tical description based on the landscape theory. We first discuss the theoretical descriptions of rapid protein folding before exploring to what extent the experiments of Mines et all, and the increasing number of other recent fast folding experiments, support this description. Finally, we consider what the two fields have taught us so far about folding and the energy landscape. The folding funnel The energy-landscape theory of folding starts with the view that folding kinetics are best considered as a progres- sive organization of an ensemble of partially folded struc- tures (Fig. l), rather than a serial progression between intermediates. Thus, to understand kinetics one must statistically characterize the number and the distribution of free energies of the members of an ensemble of protein molecules that have partial order. A few collective reaction coordinates or order parameters should suffice to organize the description of the energy landscape. In contrast to most organic chemistry reactions, where the reaction coordinates describe a single structure, here they are collective coordi- nates characterizing an ensemble of structures as in elec- tron transfer [24,25]. The search through these very large 426 Chemistry & Biology 1996, Vol3 No 6 stabilize correct (native) interactions, on average, more than one expects by chance. We thus say that fast-folding pro- teins satisfy the principle of minimal frustration [28]. On a global level, the landscape must resemble a funnel ([29-311 and J.N.O., P.G.W., Z.L.-S. & N.D. Socci, unpublished data) (Fig. 2). As a folding reaction coordinate approaches its native value, the peak in energy distribution of the ensemble of conformations shifts to a lower value, as shown in Figure 3. The mean energy of structures in the funnel goes down as the ensemble approaches the native state. Simultaneously the number of conformations decreases, reflecting a loss of configurational entropy. (The entropy of the solvent, so important for the hydrophobic force, is included in the solvent average (free) energies used in the statistical distributions characterizing the landscape.) The Figure 2 Beginning of helix formation and collapse %Biology, 1996 Network of conformational changes occurring in the ensemble of conformations for a protein as it folds to the native structure. The folding reaction coordinate Q is the average fraction of native contacts in members of the corresponding ensembles. The conformations in the transition state ensemble have a Q-value of -0.6. The intermediate structures are obtained from computer simulations of a four helix bundle with varying degrees of native character. By organizing the states by energy and CI instead of the specific intermediates, a funnel description of the folding process shown in Figure 2 is obtained. (The simulations shown here are based on associative memory Hamiltonian structure prediction algorithms and show diminished helicity at low Q.) number of configurations may present difficulties as Transition state a = 0.6 pointed out by Levinthal years ago [26,27]. Even the protein conformations with given amounts of partial order have various energies that must be described by a distribu- tion [28]. This give rise to what is called a ‘rugged energy Discrete folding intermediates landscape’, because many non-native interactions can exist I in a (partially) disordered protein structure and these can Native structure 0 Chemistry % Biology, 1996 be either favorable or non-favorable energetically. Confor- mational transitions from the deeper energy states in an The schematic funnel for a 60-amino-acid helical protein as obtained ensemble must typically surmount an energy barrier deter- from the corresponding-states principle analysis [30] with the 27-mer mined both by this energetic ruggedness, AI@, and any lattice model. The positions of the molten globule states, the transition barriers provided by stereochemical constraints inherent in state ensemble, and the local glass transition (the point at which the polypeptide backbone. discrete trapping states emerge) are shown as a function of the order parameters: fraction of native contacts 0, the solvents averaged energy E, and the configurational entropy S, which are drawn to scale. The ruggedness of the landscape is reflected in the kine- The stability gap SE, quantitates the specificity of the native contacts. tics of escape from transient conformational traps. Fast The ruggedness of the landscape A& determines the local barriers, but folding would be impossible on an entirely rugged land- its value is not the conformational diffusion activation height, which is E, - 7&T{. kB is the Boltzmann constant, Tf is the folding temperature, scape, which is called a completely frustrated landscape. is the energy of the native structure. Fast folding is only possible because of guiding forces that and Enat Review Fast folding Wolynes et al. 427 Figure 3 collective reaction coordinates such as degree of collapse and local secondary structure has been recently deve- (a) . loped (Z.L.-S., B.E. Ramirez & P.G.W., unpublished data) a=o.z (Fig. 4). In this multidimensional picture the basic scenarios 4 SE, for folding remain the same, although new characteristic Log n(E) energies such as entropy diminution and energy loss upon collapse are needed to set up the correspondence. Fast Folded folding experiments will allow the characterization of these important parameters, which determine a major part of the entropy loss, ultimately leading to folding. (W E t t a=05 The dominant dependence of folding on the stability gap (the difference in stability between folded and unfolded states) is strongly supported by the experiments of Gray, Winkler and colleagues [l&23]. Global changes to the Figure 4 Logn(E)E , a=i.o Single kinetic step Log n(E) Free energy of the specified ensemble Histograms of protein configurational states at various stages of folding. (a) Collapsed phase with Q = 0.2. (b) Transition state with C = 0.6 near the middle of the funnel. (c) Native state with 0 = 1 .O. In any given folding event, the protein molecule passes through intermediate Q states. In a type I folding, however, no significant population is trapped in these states, which are never substantially occupied kinetically. For downhill folding on fast time scales, these intermediate states may be sufficiently populated to be observed directly in the new fast-folding experiments. The y-axis is the logarithm of the number of configurations, and the x-axis is the free energy of a Molten 1 particular configuration averaged over the solvent. Native state globule 1 mean energy loss down the funnel gives an additional energy parameter characterizing the landscape topography, the stability gap 6E,. The configurational entropy loss, the third significant parameter, is called the Levinthal entropy (S,) as it is simply a logarithmic measure of the vastness of the configuration space of the biomolecule [30,32]. The fact Fraction native contacts, Q that the landscape can be described by these very few para- meters (SE,, S,, and AE) allows us to use a law of corre- The multidimensional aspect of the free-energy surface. The average free energy of the ensemble (as opposed to that of an individual sponding states (which uses scaling of certain known structure as shown in Fig. 2) is shown here as a function of the fraction parameters to directly compare two different proteins) to of native contacts, Q, and total contacts. The free energies include the understand the important characteristics of the funnel land- configurational entropy counting the number of states with a particular scape for real proteins by investigating analytical models value of the collective coordinates. The surface is plotted using the and lattice simulations [30].
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